System for ahah-based feature extraction of surface manifolds

ABSTRACT

Methods and systems for feature extraction of LIDAR surface manifolds. LIDAR point data with respect to one or more LIDAR surface manifolds can be generated. An AHAH-based feature extraction operation can be automatically performed on the point data for compression and processing thereof. The results of the AHAH-based feature extraction operation can be output as a compressed binary label representative of the at least one surface manifold rather than the point data to afford a high-degree of compression for transmission or further processing thereof. Additionally, one or more voxels of a LIDAR point cloud composed of the point data can be scanned in order to recover the compressed binary label, which represents prototypical surface patches with respect to the LIDAR surface manifold(s).

CROSS-REFERENCE TO PATENT APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/057,184, which was filed on Mar. 1, 2016, the disclosure of which isincorporated herein by reference in its entirety. U.S. patentapplication Ser. No. 15/057,184 is continuation of U.S. patentapplication Ser. No. 13/613,700, which was filed on Sep. 13, 2012, andwhich claimed priority under 35 U.S.C. 119(e) to U.S. Provisional PatentApplication Ser. No. 61/663,264, which was filed on Jun. 22, 2012, thedisclosures of which are also incorporated herein by reference in theirentireties. This patent application thus claims priority to the Jun. 22,2012 filing date of U.S. Provisional Patent Application Ser. No.61/663,264.

TECHNICAL FIELD

Embodiments are generally related to machine learning and artificialintelligence. Embodiments additionally relate to feature extractionmethods and systems. Embodiments further relate to the field of LIDAR(Light Detection And Ranging, also LADAR).

BACKGROUND

Machine learning can roughly be characterized as the process of creatingalgorithms that can learn a behavior from examples. One simple exampleis that of pattern classification. A series of input patterns are givento the algorithm along with a desired output (the label) and thealgorithm learns how to classify the patterns by producing the desiredlabel for any given input pattern. Such a method is called supervisedlearning since the human operator must provide the labels during theteaching phase. An example is the kernal-based SVM algorithm.Alternately, unsupervised “clustering” is a process of assigning labelsto the input patterns without the use of a human operator. Suchunsupervised methods must usually function through a statisticalanalysis of the input data, for example, finding the Eigen value vectorsof the covariance matrix. One such example of unsupervised clustering isthe suit of k-means algorithms.

A few problems have continued to challenge the field of machinelearning. Few if any standard md accepted methods exist for learningbased on few patterns or exemplars. Without sufficient examples, findinga solution that balances memorization with generalization is of oftendifficult. The difficulty is due to separation of a training and testingstage, where the variables that encode the algorithms learning behaviorare modified during the learning stage and tested for accuracy andgeneralization during the testing phase. Without sufficient examplesduring the leaning stage, it is difficult or impossible to determine theappropriate variable configurations leading to this optimal point.Theoretically, the mathematical technique of support-vector-maximizationprovides an optimal solution, should there be sufficient training datato encompass the natural statistics of the data and presuming thestatistics do not change over time, a problem called concept drift. Theidea is that al input patterns are projected into a high dimensionalwhere they are linearly separable space.

A linear classifier can then be used to label the data in binaryclassification task. A linear classifier can be thought of as a hyperplane in a high-dimensional space, where we cal the hyper plane thedecision boundary. A input falling on one side of the decision boundaryresults in a positive output, while al inputs on the other side resultin a negative output. The support-vectors are the distances from theclosest input points to the decision boundary. The process of maximizingthis distance is the process of support-vector-maximization. However,without sufficient examples it is of little or no use since identifyingthe support-vectors requires testing a number of input patterns to findwhich ones are closer to the decision boundary. Indeed, some thought mayconvince the reader that finding the point of optimal generalization isnot possible with only one example since by definition measuringgeneralization requires evaluation of a number of exemplars.

Another problem facing the field of machine learning is adaptation tonon-stationary statistics, i.e. concept drift. The problem occurs whenthe statistic of the underlying data changes over time. Any method thatrelies on a separation of training and testing is doomed to failure, aswhatever the algorithm has learned quickly becomes incorrect as timemoves forward. Methods for continual real-time adaptation re dearlyneeded, but such methods are often at odds with the training methodsemployed to find the initial solution.

Another problem acing the field of machine learning s power consumption.Finding statistical regularities in large quantities of streaminginformation can be incredibly power intensive, as the problem encounterscombinatorial explosions. The complexity of the task is echoed inbiological nervous systems, which are essentially communication networksthat self-evolve to doted and act on regularities present in the inputdata stream. It is estimated that there are between 2 and 4 kilometersof wires in one cubic millimeter of cortex. At 2500 cm2 total area and 2mm thick, that is 1.5 million kilometers of wire in the human cortex, orenough wire to wrap around the earth 37 times.

For this reason, the closer one can match the distributed processors ofthe hardware to the structure of the underlying network being simulated,the less information must be shuttled back-and-forth between memory andprocessor and the lower the power dissipation required for emulation.The limit of efficiency occurs when the hardware becomes the network,which occurs when memory becomes processing. We call this point physicalcomputation, since the physical properties of the system we now“computing” the answer rather than the answer being arrived atabstractly through operations on numbers represented as binary values.Physical computation is related to, but not the same as, analogcomputation. For example, consider the problem of simulating the fall ofa rock dropped from some height. We may go about a solution a number ofways. First, we may derive a mathematical expression and evaluate thison a digital computer. This is digital computing. Second, we may solve adifferential equation by noticing the equations of motions aremathematically equivalent to some other process, for example, those oftransistor physics. This is analog computing. The third option is thatwe could find a rock and drop it. This is physical computing. Relativelysimple arguments can be made to show that this is the only practicalsolution to large adaptive systems on the scale of living systems suchas a brain. Digital and analog computing each suffer from thememory-processing duality, a condition which does not exist in natureand which introduces very high power dissipations for highly adaptivelarge-scale systems.

As an example of just how significant computation is, consider IBM'srecent cat-scale cortical simulation of 1 billion neurons and 10trillions synapses. This effort required 147,456 CPU's and ran at1/100th real time. At a power consumption of 20 W per CPU, this is 3megawatts. If we presume perfect scaling, a real-time simulation wouldconsume 100× more power 300 megawatts. A human brain is ˜20 times largerthan a cat, so that a real-time simulation of a network at the scale ofa human would consume 6 GW if done with traditional serial processors.This is 600 million times more energy than a human brain actuallydissipate. It is worth consideration that every brain in existence hasevolved for just one purpose: to control an autonomous platform. Analgorithm for finding regularities in large quantities of streaminginformation that cannot be mapped directly to physically adaptivehardware will likely not find use in mobile platforms, as the energydemands far exceed practical power budgets.

BRIEF SUMMARY

The following summary of the invention is provided to facilitate anunderstanding of some of the innovative features unique to the presentinvention, and is not intended to be a full description. A fullappreciation of the various aspects of the invention can be gained bytaking the entire specification, claims, drawings, and abstract as awhole.

It is, therefore, one aspect of the disclosed embodiments to provide foran proved feature extraction method and system.

It is another aspect of the disclosed embodiments to provide for methodsand systems for feature extraction of surface manifolds from noisy pointcloud data sources.

The aforementioned aspects and other objectives and advantages can nowbe achieved as described herein. Methods and systems for featureextraction of surface manifolds are disclosed herein. In general,pixilated data with depth information can be generated via techniquesincluding, for example, LIDAR. An AHAH-based feature extractionoperation can be automatically performed on or with respect to the pointdata for compression and processing thereof. The results of theAHAH-based feature extraction operation can be output as a compressedbinary label representative of the at least one surface manifold ratherthan the point data to afford a high-degree of compression fortransmission or further processing thereof. Additionally, one or morevoxels of a point cloud composed of the point data can be scanned inorder to recover the compressed binary label, which representsprototypical surface patches with respect to the surface manifold(s).

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, in which like reference numerals refer toidentical or functionally-similar elements throughout the separate viewsand which are incorporated in and form part of the specification,further illustrate the present invention and, together with the detaileddescription of the invention, serve to explain the principles of thepresent invention.

FIG. 1 illustrates a graph depicting data indicative of a meta-stableswitch, in accordance with the disclosed embodiments;

FIG. 2 illustrates a sample graph of a Lissajous I-V curve, inaccordance with the disclosed embodiments;

FIG. 3 illustrates a schematic diagram of a synapse based on adifferential pair of memristors, in accordance with the disclosedembodiments;

FIG. 4 illustrates a schematic diagram depicting a circuit that includesplurality of AHAH nodes, in accordance with the disclosed embodiments;

FIG. 5 illustrates a schematic diagram of a group of differentialsynapses;

FIG. 6 illustrates a schematic diagram of a 2-1 post-flip AHAH circuit,in accordance with the disclosed embodiments;

FIG. 7 illustrates a data structure of four different distributions ontwo wires x0 and x1, in accordance with the disclosed embodiments;

FIG. 8 illustrates a schematic diagram of AHAH rule attractor pointsrepresenting bifurcations of its input space, in accordance with thedisclosed embodiments;

FIG. 9 illustrates a collective of AHAH nodes each occupying distinctattractor states can distinguish features, in accordance with thedisclosed embodiments;

FIG. 10 illustrates a schematic diagram of a system of AHAH nodes, inaccordance with the disclose embodiments;

FIG. 11 illustrates a block diagram of a basic module layout, where thenoisy input X0 in dimension D0 is reduced in dimensionality andconditioned to a stable bit pattern X1 in dimension D1, which is furtherreduced to a maximally efficient compact bit pattern X2 in dimension D2,in accordance with the disclosed embodiments;

FIG. 12 illustrates a graphical representation of a collection of pointsfrom a measurement representing a surface patch; and

FIG. 13 illustrates a graph indicating that LIDAR point data may beencapsulated within voxels, which can be further discretized into gridelements and used to construct an activity vector consisting of theindices to the grid elements that contain point data, in accordance withthe disclosed embodiments.

DETAILED DESCRIPTION

The particular values and configurations discussed in these non-limitingexamples can be varied and are cited merely to illustrate an embodimentof the present invention and are not intended to limit the scope of theinvention.

The embodiments will now be described more fully hereinafter withreference to the accompanying drawings, in which illustrativeembodiments of the invention are shown. The embodiments disclosed hereincan be embodied in many different forms and should not be construed alimited to the embodiments set forth herein; rather, these embodimentsare provided so that this disclosure will be through and complete, andwill fully convey the scope of the invention to those sidled in the art.Like numbers refer to like elements throughout. As used herein, the term“and/or” includes any and al combinations of one or more of theassociated listed items.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising” when used in this specification, specify thepresence of stated features, integers, steps, operations, elementsand/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The disclosed embodiments offer first a mechanism that can optimallyproduce a series of labels responding to features in an input datastream. Given a set of noisy, incomplete but re-occurring patterns, thegoal is to output stable labels for the patterns. We can achieve thisgoal by using a collection of AHAH nodes to collapse the input space toma high-dimensional and noisy input space to a low-dimensional andnoise-free space, where we can then perform exact bit matching on theoutput to further reduce the dimensionality. Because our methods can beconstructed as a physical circuit, we will proceed with a background onmemristors as met-sable switches and the AHAH plasticity rule. However,the methods may also be utilized in more traditional methods of softwareand hardware where we extract core mathematical models and simulatethese models within our computing system. The AHaH rule can beunderstood as a two-part procedure of state evaluation that results innegative feedback to the synaptic state (Anti-Hebbian Learning) followedby state reinforcement that results in positive feedback to the synapticstate (Hebbian learning). Such techniques are detailed in, for example,U.S. Pat. No. 7,599,895, which is incorporated herein by reference.

A memristor m a collection of meta-stable switches (MSS). Each MSSpossesses at least two states, A and B, separated by a potential energybarrier. This can be seen in Illustration 1. We will set the barrierpotential as the reference potential V=0. The probability that the MSSwill transition from the A state to the B state is given by P_(A), whilethe probability that the MSS will transition from the B state to the Astate is given by P_(B).

The transition probabilities [P_(A), P_(B)] can be modeled as:

$P_{A} = {{\alpha \frac{1}{1 + e^{- {\beta {({{\Delta \; V} - V_{A}})}}}}} = {{\alpha\Gamma}\left( {{\Delta \; V},V_{A}} \right)}}$P_(B) = α(1 − Γ(Δ V, −V_(B)))

In this case,

$\beta = \frac{q}{kT}$

is the thermal voltage and is equal to 28 mV⁻¹,

$\frac{\Delta \; T}{t_{c}}$

is the ratio of the time step period Δt to the characteristic time scaleof the device t_(c) and ΔV is the voltage across the device. We willdefine P_(A) as the positive-going direction so that a positive appliedvoltage increases the chances of occupying the B stale. Each state hasan intrinsic electrical conductance given by w_(A) and w_(B). A MSSpossesses utility in an electrical circuit as a memory or adaptivecomputational element so long as these conductances are different. Wewill take the convention that w_(b)≥w_(a).

FIG. 1 illustrates a graph 10 depicting data indicative of a meta-stableswitch, in accordance with the disclosed embodiments. A meta-stableswitch is a two-state device that switches probabilistically between itsstates as a function of applied bias and temperatures. A memristor is acollection of N meta-stable switches. We can model this in discrete timesteps Δt. The memristor conductance is given by the sum over eachmeta-stable switch:

W _(m) =N _(A) w _(A) +N _(A) w _(B) =N _(B)(w _(B) −w _(A))+Nw _(A)

wherein N_(A) is the number of MSS's in the A state, N_(B) is the numberof MSS's in the B stale and N=N_(A)+N_(B). At each time step somesub-population of the MSSs in the A state will transition to the Bstate, while some sub-population in the B state will transition to the Astate. The probability that k switches will transition out of apopulation of n switches given a probability of p is given by thebinomial distribution:

${P\left( {n,k} \right)} = {\frac{n!}{{k!}{\left( {n - l} \right)!}}{p^{k}\left( {1 - p} \right)}^{n - k}}$

As n becomes large, we may approximate the binomial distribution with anormal distribution:

${G\left( {\mu,\sigma^{2}} \right)} = {\frac{1}{\sqrt{2{\pi\sigma}^{2}}}e^{\frac{- {({x - \mu})}^{3}}{2\sigma^{2}}}}$

wherein μ=np and σ²=np(1−p).

The change in conductance of a memristor is a probabilistic processsince a memristor is composed of discrete meta-stable switches. Usingthe approximation above, the number of MSSs that transition between Aand B states is picked from a normal distribution with a center at npand variance np(1−p), where the state transition probabilities are givenas above.

The update to the memristor conductance is thus given by thecontribution from two random variables picked from two normaldisruptions:

ΔN _(B) =G(N _(A) P _(A) ,N _(A) P _(A)(1−P _(A)))−G(N _(B) P _(B) ,N_(B) P _(B)(1−P _(B)))

The update to the conductance of the memristor is then given by:

Δw _(m) =ΔN _(B)(w _(B) −w _(A))

To measure the characteristic timescale of the device one may initializea memristor into a non-equilibrium state such as N_(B)=N or N_(B)=0 andmeasure the decay back to an equilibrium conductance period under zerobias.

FIG. 2 illustrates a sample graph 20 of a Lissajous I-V curve, inaccordance with the disclosed embodiments. Graph 20 plots output currentalong the y-axis versus input voltage along the x-axis for a samplememristor in graph 20. A memristor is intrinsically a stochasticelement, although composed of many MSS's or subjected to large voltagedifferentials it may appear to be deterministic. Depending on therelative values of V_(A) and V_(B) the device will display a range ofcharacteristics. Of some interest is the property of decay and anon-conducting ground state, which we can be achieved under theconditions V_(B)<V_(A). V_(A)⇒kTlq and w_(B)>w_(A).

The utility of a memristor lies in its ability to change its conductanceas a function of the voltage history applied to the device. This can beillustrated by a Lissajous I-V curve that shows how the conductance of amemristor changes over time as a sinusoidal voltage is applied.

The core device element of our self-organizing unit or node is thus themeta-stable switch, and a memristor can be seen as a device composed ofa collection of meta-stable switches. A synapse is a differential pairof memristors: W=w₀−w₁, where W denotes the difference in conductancebetween the two memristors composing the synapse.

FIG. 3 illustrates a schematic diagram of a synapse 30 based on adifferential pair of memristors 32, 34, in accordance with the disclosedembodiments. The configuration shown in FIG. 3 is not unique and in factthere exists three possible configurations 2-1, 1-2, and 2-2, whichrefers to the number of input and output electrodes on the synapse. Afurther discussion of possible arrangements can be found in U.S. Pat.No. 7,509,805 entitled “Methodology for the Configuration and Repair ofUnreliable Switching Elements,” which issued on Oct. 6, 2009, and isincorporated herein by reference in its entirety.

The probability that a meta-stable switch will transition from itsground state to excited state is a function of the applied voltage andtime it is applied. For this treatment, we will take the function to bequadratic in voltage and linear in time as indicated by the followingequation:

P(E ₀ →E ₁)≈αV ² T

In the above equation, the variable a represents a constant and thevariable T represents a characteristic update timescale. We are thuspresuming that both applied voltage polarities will cause the memristorto increase its conductance. This is the case in memristors composed ofnanoparticles in a colloidal suspension, but is not true of almemristors, for example, those reported by Hewlett-Packard, Universityof Boise, University of Michigan, UCLA, and others. In these cases, areverse voltage polarity will cause a decrease in device conductance.This is shown above in FIG. 2.

Furthermore, a memristor may act as an intrinsic diode. This is true ofsome memristors, for example, that reported of U of M, but not of others(U of B). We may thus categorize the various types of memristors aspolar or non-polar in regards to their ability to change conductance asa function of the applied voltage and rectifying or non-rectifying as afunction of their intrinsic (or not) diode properties. Our methods applyto al such configurations, although various synaptic configurations(1-2, 2-1, 2-2) must be used. Furthermore, a mechanisms for lowering theconductance of the device must be available, be it a reverse bias,application of high frequency AC voltage, or simply to let it decay overtime.

FIG. 4 illustrates a schematic diagram depicting a circuit 40 thatincludes a plurality of AHAH nodes 42, 44, and 46, in accordance withthe disclosed embodiments. Circuit 40 further includes memristors 50 to84. An AHAH node is a collection of synapses and associated CMOSfeedback circuitry acting on one of the three possible electrodeconfigurations of 1-2, 2-1 or 2-2. We will illustrate the 2-1 case belowfor non-rectifying polar memristor. Synapses are formed at theintersection of output and input electrodes. A synapse is a differentialpair of memristors between the two output electrodes and one inputelectrode, as shown in illustration 3. A node is formed from many suchsynapses connecting many inputs to the node's electrode, as seen in FIG.4. Many such circuits are possible for providing feedback voltage to thenode's electrode. Before we discuss one possible circuit, let us turnour attention to one node and recognize it as a series ofvoltage-divider circuits formed from the input lines.

FIG. 5 illustrates a schematic diagram of a group of synapses, whereineach synapse can be seen as a voltage divider prior to application offeedback voltage, in accordance with the disclosed embodiments. Thesample synapses shown in FIG. 5 include memristors 52, 54, and 56, 58,and 60, 62. In general, the AHAH rule is composed of two basic phases:Evaluate and Feedback. During the evaluate phase, input voltages areapplied and these voltage are integrated via the differential synapseson the nodes electrode. The evaluation phase is a passive process and,in the case of the 2-1 configuration, consists of solving for thesteady-state voltage. It should be noted that during this phase, eachsynaptic state undergoes negative feedback.

For example, suppose a memristor was highly positive so that: w₀>>w₁.This will have the effect of puling the electrode voltage (V inillustration 3 and 5) up, reducing the voltage drop across the w₀memristor but increasing it over the w₁ memristor. This will cause thew₁ to increase its conductance more than the w₀ memristor, thus movingthe synapse back toward the zero-point. During the feedback phase,positive feedback is applied to the electrode via a voltage-keepercircuit. During the feedback phase, the synapse undergoes an update thatis opposite in direction to that which it received during the evaluationphase and it proceeds for a variable time. This can be seen morn clearlyin FIG. 6, where we have identified four basic phases, θ0 through θ3,labeled charge, evaluate, feedback and decay, respectively.

FIG. 6 illustrates a schematic diagram of a 2-1 post-flip AHAH circuit70, in accordance with the disclosed embodiments. Circuit 70 generallyincludes memristors 72, 74 and a voltage source 76 (V_(∞)/2), along withcomponents 78, 80 and inverters 82, 84, and 86. A graph 88 also shown inFIG. 6, tracks charge, evaluate, feedback, and decay phases. In the mostgeneral case, we may omit the evaluate and decay phase, although weinclude them here for clarity. In the charge phase, voltages are appliedto the inputs and the post-synaptic voltage is evaluated via a passiveintegration. In the evaluate phase, the positive-feedback voltage keepercircuit controlled with clock C0 is activated. During feedback, thepost-synaptic voltage is inverted through deactivation of passgate withclock C1 and activation of inverter C2.

During the decay phase, the inputs are inverted and the post-synapticelectrode voltage is set to ½ of the supply voltage such that eachmemristor receives equal and opposite voltage and thus achieves anaccelerated decay. This phase may be removed from each clock cycle, foreexample, occurring once every 100-cock cycles, or should the memristorhave a natural decay rate commiserate with the usage, a period of sleepwould suffice. One can see how many strategies are available toaccomplish the basic anti-hebbian and hebbian phases of the AHaH rule.

The operation of the device can be seen as a “memory read” and “memoryrefresh cycle”, where the act of read (evaluate) damages the synapticstates, while feedback repairs the state. It is obvious that eachmemristors conductance would saturate over time if not reduced. This canbe accomplished by adding a decay phase in the cycle as shown, byproviding for a sufficiently long rest-state to allow the memristor todecay, or to force the decay by applying an equal-magnitude reverse biasacross both memristors after a set or variable number of cycles.Although there are multiple ways to accomplish the task, the net effectis simply to decay the memristors to keep them operating within theirdynamic range and to prevent saturation over time. We call such anoperation a synaptic normalization. As the dynamic range of thememristors increases, the frequency of synaptic renormalization may bereduced.

A fundamentally important property to the AHAH rule is that a themagnitude of the post-synaptic activation becomes large, the Hebbianportion of the update must decrease in magnitude or transition toAnti-Hebbian. This property insures the rule converges to independentcomponents of the data that is being processed by the AHaH node.

Let us now take a step back and discuss data structure before againfocusing on specific circuit in implementations. Suppose that we took amonochromatic digital picture of page of text. By arranging the pixelsin proper rows and columns, you would of course be able to perceive thetext and understand what is written. However, the underlying datastructure is not letters, its binary pixels. By simply taking the arrayof pixels and arranging them into any other pattern, what was coherentis now an incoherent jumble of bits. The conclusion is that thestructure of the information (the letters) is not the same as theinformation channels that carry the information (the pixels). Theprimary unction of unsupervised clustering algorithms is to uncover thestructure of the information.

Two wires carrying the same sequence of bits do not carry any additionalinformation. This is captured in the concept of mutual information,which measures how much one signal tells us about another signal. If themutual information between wire A and wire B was 1, for example, theycarry the same information. If the mutual information is zero, then theyare independent. Let us visualize this with respect to FIG. 7, whichillustrates a data structure 90 of four different distributions on twowires X0 and X1, in accordance with the disclosed embodiments. Thesefour different distributions are: (I) The mutual information between X0and X1 is 1 and no states can be distinguished; (II) Two states can beresolved; (III) Three states can be resolved; and (IV) Nine states canbe resolved.

We must make two important points about FIG. 7. First, the number ofstates carried by the wires is in general unrelated to the number ofwires that carry the information. For binary encodings the total numberof resolvable states over N wires is as high as 2^(N) but likely muchlower. The first challenge in unsupervised clustering or learningalgorithms is to resolve underlying states from observations over time.Second, wires that do not resolve more than one state are useless. Thinkabout it this way: suppose you were locked in a room and could onlyobserve two light bulbs. If the light bulbs were always on, a in (I) ofIllustration 7, they can convey no information. On the other hand, ifone of the light bulbs blinked on and off while the other remained on asin (II), you could distinguish between two states. Something must becausing that light to turn n and off. There must be a source. What isthe nature of this source? A answer to this question gets at the heartof machine learning. Suppose that our observations of two wires X0 andX1 led to the distribution of II. Furthermore, once we recognized theexistence of two states we took note of their specific sequence overtime: ABAABBABAABB . . . .

We can now identify further temporal structure. It is temporal structurethat allows us to infer the existence of a source or mechanism in theenvironment since temporal events link cause and effect. Explaining atemporal sequence requires a model of a mechanism that generates thesequence. We could analyze the sequence in a number of ways. Forexample, the sequence AA follows AB, BB follows AA, and AA follows AB,repeating in a cycle. On the other hand, the sequence ABAABB is simplyrepeating, or ABB follows ABA. How we view the sequence is dependent onthe temporal window we are capable of holding in memory. This leads usto an important simplifying observation. The sequence above is notactually a sequence! It is spatial pattern. After al, what we havecommunicated is static letters on a page, not a temporally dynamic videoor song. By recording in temporary memory prior states, we haveconverted a temporal pattern into a spatial pattern. The problem ofidentifying temporal structure then becomes the problem of identifyingspatial structure.

It is this realization that has spawned the recent work in liquid andecho state machines, which have been used successfully inpredicting/modeling time sequences and learning temporal transferfunctions. A rock thrown into a still pond will create ripples such thattaking a picture at any instant in time enables the past to bereconstructed. Temporal structure is converted into spatial structurewhen information travels through networks of path-day.

Suppose we created a large dynamic network as in biological cortex,where pulses traveled between nodes over links. The fact that a signaltakes time to propagate over a link introduces a time-delay in thenetwork and at this moment temporal structure is converted into spatialstructure.

For example, two pulses that arrive at the same time at a node couldhave taken two paths: one path could come directly from a sensory inputwhile the other could have come from another sensory input multipletimes steps in the past. What the node perceives as a spatialcoincidence is actually a prediction, and the prediction has beenencoded as links in the network.

Recall from the discussion of FIG. 7 that only states which can bedistinguished are useful. A light that is always on or always off cancommunicate no information and can serve no useful function to anetwork. Extraction of data structure would then appear to be related toa fundamental operation of differentiation between states over time.Given some time series of vectors P=[p₀, p₁, p₂, . . . p_(n)], whatspatial states can be distinguished? What are the spatial buildingblocks? Technically, we are looking for the extraction of signals thatare independent in time.

If an input pattern falls on one side of the decision boundary, theoutput of the AHAH node is positive, while it is negative if it is onthe other side of the boundary. Stated another way, the node output isan efficient binary encoding representing one natural independentcomponent of the input data distribution. It is clear that a single AHAHnode cannot alone become selective to a particular feature or pattern inthe data. For example, consider the distribution of IV in FIG. 8. Thereare nine input data states or patterns. An AHAH node may only bifurcateits spaces it can only output a binary label. However, a collective ofAHAH nodes each occupying different states can, as a group, distinguisheach feature.

Suppose we had two input wires that carried a sequence of vectors that,over time, matched the distribution of IV in FIG. 8. These two inputsconnect to four AHAH nodes 1-4. One such configuration is seen in FIG.9. FIG. 9 illustrates a collective 102 of AHAH nodes each occupyingdistinct attractor states that can distinguish features. Lines 1-4 inFIG. 9 represent the decision boundaries of AHAH node 1-4. Features A-Ifall in FIG. 9 on various sides of each node decision boundary so thateach feature results in a unique binary output tom the group of AHAHnodes

Given some input pattern, we can simply read off the output value ofeach node as a binary label that encodes each unique feature. Feature Agets the binary label 0011 because node 1 output is negative, 2 isnegative, 3 is positive, and 4 is positive.

In such a way, a collective of AHAH nodes serves as a “partitioning” or“clustering” algorithm, outputting a unique binary label for each uniquestatistically independent input source, regardless of the number ofinput lines that carry the data.

The core operation of a collective of AHAH nodes can be seen inIllustration 10. Many sparse binary (spiking) inputs synapse onto asmall collection of AHAH nodes. Each temporally correlated group ofinputs forms independent components (IC) and the AHAH rule binds theseinputs together by assigning them synapses of the same sign. InIllustration 10 we can see six IC's with positive weights shown as greenand negative as red. The space of allowable AHAH states 2^(F), wherein Fis the number of input features (i.e. patterns). However, we do not wisheach AHAH node to occupy every state. There is a possibility of alweights staling the same sign, a condition we call the null state. Thenull state is useless computationally as the node's output neverchanges. To prevent occupation of the null states, we include a biasinput that is always active and only ever receives anti-Hebbian updates.

To emphasize the general quality of the AHAH rule and its applicabilityoutside of purely physically adaptive circuits, we will re-state therule in a more generic mathematical form:

□w _(t) =x _(i) f(y)

y=Σ _(t=0) ^(N) x _(i) w _(t) +x _(bias) w _(biax)

f(y)=

+

·sign(y)

□w _(bias)=

In the above formulation/equations, α, β, and γ are constants, x_(i) isthe ith input and w_(i) is the ith weight. We may generally presume thatthe inputs are binary and sparse so that only small subset, perhaps 10%or less, of inputs are active. Seen in this light, we may simply set x=1for all active inputs and state that weights are modified when they areused and not otherwise. The bias can be seen as an input that is alwaysactive, and the update to the bias weight can be seen as purelyanti-Hebbian. The constants control the relative contribution ofpositive and negative feedback and may be modified to achieve certaindesired results. For example, by increasing the contribution of the biasinput, we may increase a “restoring force” that forces the AHAH nodeinto a state that bifurcates its input space. The net effect is asubtraction of an adaptive average. If the node has found an attractorstate that spits its space in half, such that approximately half of theIC's are given positive weights and half are given negative weights, theaverage node output will be zero and the bias weight will be zero. Ifthe output becomes unbalanced, the bias will bring it back, thuspreventing the occupation of the null state.

FIG. 10 illustrates a schematic diagram of a system 104 of AHAH nodes.As shown in FIG. 10, the core operation of the collection or system 104of AHAH nodes is spatial pooling of input lines into temporallyindependent components (IC), collapsing the large input space, andoutputting stable binary labels for input features.

Once each AHAH node has settled into unique attractor stabs, thecollective will output a binary label for each input feature, convertinglarge, sparse, incomplete, noisy patterns into small, complete,noise-free binary patterns. There are various ways of describing thisoperation in the literature. We are performing a spatial pooling in thatwe are collapsing a large set of input patterns into a much smaller set,also known as clustering. We emphasize, however, that we a certainly notclustering in the traditional sense.

Methods like k-means perform projection operations, taking an inputvector and projecting it onto the weight vectors of each node. The nodewith the best match is considered the “winner” and its weight vector ismoved closer to the data pattern. The projection operation is criticallydependent on the number of nodes used, which we believe to be anincorrect starting assumption. This is like presuming an absolutereference frame on the incoming data statistics. Given 10 nodes, thedata will be projected onto a 10-dimensional coordinate axis, regardlessof the natural statistics of the data. Our microcircuit is like arelative reference frame, where a feature is only distinguishablebecause it is not another feature. If the underlying dimensionality ofthe feature space is 10, for example, our circuit will output 10 labelsindependent of the number of AHAH nodes.

We are generating labels (L) for features (F). Let us presume that eachAHAH node will randomly assign each IC to either the positive ornegative state. The total number output labels is 2^(N), where N is thenumber of AHAH nodes. If N is small and the number of features high, itis possible that the AHAH node collective will output the same label fordifferent features. However, as the number of nodes increases, theprobability of this occurring drops exponentially. Specifically, theprobability P that any two features will be assigned the same binarylabel goes, as:

$P = {{\frac{1}{2^{N}} + \frac{2}{2^{N}} + \ldots + \frac{F}{2^{N}}} = {\frac{\sum\limits_{0}^{F}i}{2^{N}} = \frac{F^{2} + F}{2^{N + 1}}}}$

For 64 features and 16 nodes, the probability of two nodes beingassigned the same label is 3%. By increasing N to 20 we can reduce thisto only 0.4% and with 32 nodes it is less than one in a million.

Let us suppose for our purposes that we have 16 nodes so that the outputof the collective is a stable 16-bit pattern. Each of the 16 bitpatterns represents a feature. Although the space of possible patternsis 2¹⁶, only a small subset will ever occur if the data is structured.However, far from noisy and incomplete, the bit patterns are stable andcan therefore be matched exactly. A further reduction from 16 bits to,for example 8 bits can be accomplished through the use ofcontent-addressable memory (CAM) or a least-recently-used-cache (LRUC)or other common methods. For example, given a set of 256 patterns wetore patters as rows and mach new patterns bit-or-bit against newpatterns.

Let us now return to FIG. 4, which depicts an array of M AHAH nodes(AHAH₁, AHAH₂, . . . , AHAH_(n)) receiving inputs from an array ofinputs (X₁, X₂, . . . , X_(N)) and producing an output on a register Rwith values (R₁, R₂, . . . , R_(n)). The output of this register is abinary bit pattern of length M, which we may feed into a CAM to furtherreduce its dimension. This can be seen in FIG. 11, which shows the AHAHmodule providing an output that is input to the CAM module.

Content-Addressable Memory (CAM) is well known in the art, and may suchembodiments are possible. Indeed, many variations may be used in thecircuit described in FIG. 11.

FIG. 11 illustrates a block diagram of a basic module layout 120, wherethe noisy input X0 in dimension D0 is reduced in dimensionality andconditioned to a stable bit pattern X1 in dimension D1, which is furtherreduced to a maximally efficient compact digital encoding in dimensionD2, in accordance with the disclosed embodiments.

The basic operation of our circuit is thus as follows: A large andlikely sparse input is presented to a synaptic matrix of AHAH nodes,termed the AHAH module, which operates the AHAH plasticity rule via theevaluate-feedback cycle as discussed. A bias input line is modulatedsuch that the bias weights do not receive the Hebbian portion of theweight update during the feedback cycle, which prevents occupation ofthe null state. The collection of AHAH nodes or AHAH module 122 fallrandomly into their attractor states, which act to bifurcate their inputspace. The output of the AHAH module 122 forms a stable bit pattern,which is then provided as an input to a Content-Addressable Memory orCAM 124 for further reduction of dimensionality. The output of the CAM124 thus provides maximally efficient binary labels for the regularitiespresent in the input to the AHAH module 122.

The methods developed above are ideally suited for applications wherelarge quantities of spares data must be encoded into discrete packets orlabels and transmitted over a wire. One such application is theidentification of LIDAR surface manifolds from point cloud data. Ratherthan transmitting, for example, 1000 points of data we may only transmitthe binary label for the particular surface patch that these points arerevealing.

LIDAR (Light Detection And Ranging, also LADAR) is an optical remotesensing technology that can measure the distance to, or other propertiesof a target by illuminating the target with light, often using pulsesfrom a laser. LIDAR technology has application in geomatics,archaeology, geography, geology, geomorphology, seismology, forestry,remote sensing and atmospheric physics as well as in airborne laserswath mapping (ALSM), laser altimetry and LIDAR contour mapping.

The acronym LADAR (Laser Detection and Ranging) is often used inmilitary contexts. The term “laser radar” is sometimes used, even thoughLIDAR does not employ microwaves or radio waves and therefore is notradar in the strict sense of the word.

LIDAR uses ultraviolet, visible, or infrared light to image objects andcan be used with a wide range of targets, including non-metallicobjects, rocks, rain, chemical compounds, aerosols, clouds, and evensingle molecules. A narrow laser beam can be used to map physicalfeatures with very high resolution.

LIDAR has been used extensively for atmospheric research andmeteorology. Downward-looking LIDAR instruments fitted to aircraft andsatellites are used for surveying and mapping, a recent example beingthe NASA Experimental Advanced Research LIDAR. In addition, LIDAR hasbeen identified by NASA as a key technology for enabling autonomousprecision safe landing of future robotic and crewed lunar landingvehicles.

Wavelengths in a range from about 10 micrometers to the UV (ca. 250 nm)are used to suit the target. Typically light is reflected viabackscattering. Different types of scattering are used for differentLIDAR applications; most common are Rayleigh scattering, Mie scattering,and Reman scattering, as well as fluorescence. Based on different kindsof backscattering, the LIDAR can be accordingly called Rayleigh LIDAR.Mie LIDAR, Reman LIDAR, Na/Fe/K Fluorescence LIDAR, and so on.

Suitable combinations of wavelengths can allow for remote mapping ofatmospheric contents by looking for wavelength-dependent changes in theintensity of the returned signal.

FIG. 12 illustrates a graphical representation of a collection of pointsfrom a LIDAR measurement(s) representing a surface patch 130, inaccordance with the disclosed embodiments. As may now be appreciated,LIDAR is a well-developed technology that is used to image an area bysending out pulses of light and detecting the backscattered radiation.This technology produces what has become known as a “point cloud” datarepresentation that consists of a collection of many points, where eachpoint has an “XYZ” coordinate and perhaps more attributes such as color.If one breaks the LIDAR data into smaller “3D pixels” or voxels of acertain size, one is left with a volume of space and a collection ofLIDAR points that encode one or more surfaces. As the size of the voxelis reduced, the number of LIDAR points captured in the space will ofcourse reduce. Although LIDAR produces a point representation, what isactually being sense is a surface in many cases. It is thereforeadvantageous to have efficient mechanisms for converting the pointrepresentation in a more compact “surface patch” representation fortransmission and further processing.

The methods we have illustrated for spontaneous extraction of featuresvia a collation of AHAH nodes may be employed for extraction andencoding of surface patches from LIDAR point data. The LIDAR point datamay be broken down into two parts. First we may decompose thethree-dimension space into voxels. Each voxel is composed of a number ofelement volume elements that may contain one or more LIDAR points. Weare showing the 2D representation for convenience. It can be easily seenhow the LIDAR point data may be represented as a voxel with active andinactive grid elements, where a voxel grid element is considered activeif it contains within its spatial boundary one or more LIDAR points. Theoutput of the voxel is thus a sparse binary activation vector consistingof the grid elements that are active.

FIG. 13 illustrates a graph 134 indicating that LIDAR point data may beencapsulated within voxels, which can be further discretized into gridelements and used to construct an activity vector consisting of theindices to the grid elements that contain LIDAR point data, inaccordance with the disclosed embodiments. In the example shown in FIG.13, grid elements [12, 14, 17, 19, 23, 29, 40] are active. We may thusactivate the corresponding input lines of an AHAH feature extractionsystem as in the configuration shown in FIG. 11. By scanning the voxelover the LIDAR point cloud, we may recover binary labels that representprototypical surface patches. The output is thus a compressed binarylabel representative of a surface manifold rather than the point data,thus affording a high degree of compression for transmission or furtherprocessing.

It can be appreciated that the exact features, which are producedthrough this technique, are dependant on the statistics of the LIDARdata, which in turn are a direct reflection of the statistics of theenvironment it is measuring. Thus, the method has the potential toprovide a high degree of compression due to the fact that the basedfeatures extracted represent the characteristic features of theenvironment. For example, the commonly occurring surface features withina city are largely flat and orthogonal to the ground whereas featureswithin a more nature environment like a forest may occur at manyorientations.

Based on the foregoing, it can be appreciated that a number ofembodiments, preferred and alternative, are disclosed herein. Forexample, in one embodiment, a method for feature extraction of surfacemanifolds can be implemented. Such a method can include the steps orlogical operations of, for example, generating point data with respectto at least one surface manifold, and performing an AHAH-based featureextraction operation on the point data for compression and processingthereof.

In another embodiment, a step or logical operation can be implementedfor scanning at least one voxel of a point cloud composed of the pointdata in order to recover the compressed binary label which representsprototypical surface patches with respect to the at least one surfacemanifold. In still another embodiment, the aforementioned point data caninclude LIDAR point data and the surface manifolds comprise LIDARsurface manifolds. In still another embodiment, the LIDAR point data iscapable of being represented as a voxel with at lead one active gridelement and at least one inactive grid element, wherein a voxel gridelement is considered active if the voxel grid element contains within aspatial boundary thereof at least one LIDAR point. In anotherembodiment, the output of the voxel can comprise a sparse binaryactivation vector including grid elements that are active.

In another embodiment, a method for feature extraction can beimplemented. Such an method can include, for example, the steps orlogical operations of presenting an input to an AHAH module comprising aplurality of AHAH nodes, wherein e plurality of AHAH nodes operatesaccording to an AHAH plasticity rule; and providing an output of theAHAH module as an input of a content addressable memory for furtherreduction of dimensionality, wherein an output of the content-dressablememory provides maximally efficient binary labels for features presentin the input to the AHAH module. In another embodiment, the plurality ofAHAH nodes can fall randomly into attractor states which act tobifurcate input space. In yet another embodiment, steps or logicaloperations can be implemented for providing a link structure so as toconvert temporally phase-shifted activations patterns into temporallycorrelated activations and extracting binary labels of features of thetemporally correlated activations for compression or processing thereof.

In another embodiment a system for feature extraction of surfacemanifolds can be implemented. Such a system may include, for example, aprocessor; a data bus coupled to the processor and a computer-usablemedium embodying computer program code, the computer-usable medium beingcoupled to the data bus. The computer program code can includeinstructions executable by the processor and configured for generatingpoint data with respect to at least one surface manifold; and performingan AHAH-based feature extraction operation on the point data forcompression and processing thereof. In another embodiment, suchinstructions can be further configured for scanning at least one voxelof a point cloud composed of the point data in order to recover thecompressed binary label which represents prototypical surface patcheswith respect to the at least one surface manifold. In anotherembodiment, the point data can constitute LIDAR point data and thesurface manifolds can constitute LIDAR surface manifolds. In otherembodiments, the LIDAR point data is capable of being represented as avoxel with at least one active grid element and at least one inactivegrid element, wherein a voxel grid element is considered active if thevoxel grid element contains within a spatial boundary thereof at leastone LIDAR point. In another embodiment, the output of the voxel cancomprise a sparse binary activation vector including grid elements thatare active.

In sill another embodiment, a system for feature extraction can beimplemented. Such a system can include, for example, an AHAH modulecomprising a plurality of AHAH nodes, wherein the plurality of AHAHnodes operates according to an AHAH plasticity rule and wherein an inputis presented to the AHAH module, and a content-addressable memory,wherein the output of the AHAH can be provided as an input of thecontent-addressable memory for further education of dimensionality,wherein an output of the content-addressable memory provides maximallyefficient binary labels for features present in the input to the AHAHmodule.

In another embodiment, the plurality of AHAH nodes can fall randomlyinto attractor states which ad to bifurcate their input space. Inanother embodiment, a link structure can convert temporallyphase-shifted activations patterns into temporally correlatedactivations. In another embodiment, an extractor can be provided forextracting binary labels of features of the temporally correlatedactivations for compression or pro ing of.

In another embodiment, a link structure can be provided, which convertstemporally phase-shifted activations patterns into temporally correlatedactivations, wherein binary labels of features of the temporallycorrelated activations are extractable for compression or processingthereof.

In still another embodiment, a system for feature extraction of surfacemanifolds can be implemented. Such a system can include a means forgenerating point data with respect to at least one surface manifold anda means for performing an AHAH-based feature extraction operation onsaid point data for compression and processing thereof. In anotherembodiment, such a system may include a means for scanning at least onevoxel of a point cloud composed of said point data in order to recoversaid compressed binary label which represents prototypical surfacepatches with respect to said at least one surface manifold. In anotherembodiment of such a system, the point data can include LIDAR point dataand said surface manifolds can include LIDAR surface manifolds. Inanother embodiment of such a system, the LIDAR point data is capable ofbeing represented as a voxel with at least one active grid element andat least one inactive grid element, wherein a voxel grid element isconsidered active f said voxel grid element contains within a spatialboundary thereof at least one LIDAR point. In yet another embodiment ofsuch a system, the output of said voxel can include a sparse binaryactivation vector including grid elements that are active.

It will be appreciated that variations of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or application. It will alsobe appreciated that various presently unforeseen or unanticipatedalternatives, modifications, variations or improvements therein may besubsequently made by those skilled in the art which are also intended tobe encompassed by the following claims.

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 21. A system for feature extraction of surface manifolds,said system comprising: an optical remote sensor that measures adistance to and/or other properties of a target by illuminating saidtarget with light, wherein said optical remote sensor provides sensordata indicative of at least one surface manifold and wherein point datais generated with respect to said at least one surface manifold; and anAHAH (Anti-Hebbian and Hebbian) based mechanism that performs anAHAH-based feature extraction operation on said point data forcompression and processing thereof.
 22. The system of claim 21 whereinsaid optical remote sensor comprises a LIDAR (Light Detection andRanging) device.
 23. The system of claim 21 further comprising scanningat least one voxel of a point cloud composed of said point data in orderto recover said compressed binary label which represents prototypicalsurface patches with respect to said at least one surface manifold. 24.The system of claim 21 wherein said point data comprises LIDAR pointdata and said surface manifolds comprises LIDAR surface manifolds. 25.The system of claim 24 wherein said LIDAR point data is capable of beingrepresented as a voxel with at least one active grid element and atleast one inactive grid element, wherein a voxel grid element isconsidered active if said voxel grid element contains within a spatialboundary thereof at least one LIDAR point.
 26. The system of claim 24wherein said output of said voxel comprises a sparse binary activationvector including grid elements that are active.
 27. The system of claim21 further comprising a plurality of meta-stable switches, said AHAHbased mechanism comprising said plurality of meta-stable switches. 28.The system of claim 27 wherein each meta-stable switch among saidplurality of meta-stable switches comprises a self-organizing unit. 29.The system of claim 27 wherein each meta-stable switch among saidplurality of meta-stable switches comprises a self-organizing node. 30.The system of claim 27 wherein said AHAH based mechanism comprises atleast one memristor composed of said plurality of meta-stable switches.31. The system of claim 21 wherein said AHAH based mechanism comprisesat least one AHAH node comprising a plurality of synapses and associatedfeedback circuitry.
 32. The system of claim 31 wherein said associatedfeedback circuitry acts on at least one of three electrodeconfigurations including a 1-2 electrode configuration, a 2-1 electrodeconfiguration, or a 2-2 electrode configuration.
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